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Diffstat (limited to 'mutagenicity.md')
| -rw-r--r-- | mutagenicity.md | 4 |
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diff --git a/mutagenicity.md b/mutagenicity.md index 9efecfd..3cba82b 100644 --- a/mutagenicity.md +++ b/mutagenicity.md @@ -42,7 +42,7 @@ Abstract Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (`lazar`) algorithms, were applied to new *Salmonella* mutagenicity dataset with 8309 unique chemical structures. The best prediction accuracies in -10-fold-crossvalidation were obtained with `lazar` models and Mol, that gave accuracies +10-fold-crossvalidation were obtained with `lazar` models and MolPrint2D descriptors, that gave accuracies ({{lazar-high-confidence.acc_perc}}%) similar to the interlaboratory variability of the Ames test. **TODO**: PA results @@ -753,7 +753,7 @@ A new public *Salmonella* mutagenicity training dataset with 8309 compounds was created and used it to train `lazar`, R and Tensorflow models with MolPrint2D and PaDEL descriptors. The best performance was obtained with `lazar` models using MolPrint2D descriptors, with prediction accuracies -({{lazar-high-confidence.acc}}) comparable to the interlaboratory variability +({{lazar-high-confidence.acc_perc}}%) comparable to the interlaboratory variability of the Ames test (80-85%). Models based on PaDEL descriptors had lower accuracies than MolPrint2D models, but only the `lazar` algorithm could use MolPrint2D descriptors. |